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--- |
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license: mit |
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tags: |
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- image-classification |
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- resnet50 |
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- medical |
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- acne-detection |
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task: |
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- image-classification |
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output: |
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- label: "level1" |
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score: 0.98 |
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widget: |
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- text: "example_image.jpg" |
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output: |
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- label: "level3" |
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score: 0.85 |
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--- |
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# ResNet-50 Model for Acne Severity Classification |
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This is a fine-tuned ResNet-50 model designed to classify the severity of acne from medical images into five categories (Severity 1 to Severity 5). The model leverages transfer learning on ResNet-50 pre-trained on ImageNet and adapts it for acne severity classification tasks. |
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--- |
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## Model Overview |
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## Model Details |
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### Key Features |
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- **Input:** Medical images of acne-affected skin. |
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- **Output:** Severity classification with one of the following labels: |
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- `level0` (No acne or minimal severity) |
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- `level1` (Mild severity) |
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- `level2` (Moderate severity) |
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- `level3` (Severe or advanced acne) |
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### Training Details |
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- **Framework:** PyTorch |
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- **Base Model:** ResNet-50 (pretrained on ImageNet) |
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- **Dataset:** A balanced dataset of acne images annotated with severity levels (Severity 1 to 5). |
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- **Preprocessing:** Images resized to 224x224 pixels, normalized using ImageNet statistics (mean: `[0.485, 0.456, 0.406]`, std: `[0.229, 0.224, 0.225]`). |
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- **Optimizer:** Adam with a learning rate of 0.001. |
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- **Loss Function:** CrossEntropyLoss. |
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- **Epochs:** 10. |
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- **Validation Accuracy:** 0.85 (on a held-out validation set). |
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--- |
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## How to Use the Model |
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## Intended Use |
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This model is intended for educational purposes and demonstrates image classification for medical images. It should not be used for clinical decision-making without further validation. |
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## Example Usage |
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You can use this model via the Hugging Face Transformers pipeline for inference. Ensure you have the `transformers` library installed: |
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```bash |
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pip install transformers |
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``` |
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### Hugging Face Inference API |
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You can use the model via the Hugging Face Inference API by sending an image encoded in base64. Here’s an example: |
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```bash |
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curl -X POST https://api-inference.huggingface.co/models/YOUR_MODEL_NAME \ |
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-H "Authorization: Bearer YOUR_API_KEY" \ |
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-H "Content-Type: application/json" \ |
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-d '{"inputs": "BASE64_ENCODED_IMAGE"}' |
